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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

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Perceptual Image Quality Assessment with Transformers

Perceptual Image Quality Assessment with Transformers

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cheon, Manri Yoon, Sung-Jun Kang, Byungyeon Lee, Junwoo LG Elect Seoul South Korea

ISBN: (纸本)9781665448994

In this paper, we propose an image quality transformer (IQT) that successfully applies a transformer architecture to a perceptual full-reference image quality assessment (IQA) task. Perceptual representation becomes more important in image quality assessment. In this context, we extract the perceptual feature representations from each of input images using a convolutional neural network (CNN) backbone. The extracted feature maps are fed into the transformer encoder and decoder in order to compare a reference and distorted images. Following an approach of the transformer-based vision models [18, 55], we use extra learnable quality embedding and position embedding. The output of the transformer is passed to a prediction head in order to predict a final quality score. The experimental results show that our proposed model has an outstanding performance for the standard IQA datasets. For a large-scale IQA dataset containing output images of generative model, our model also shows the promising results. The proposed IQT was ranked first among 13 participants in the NTIRE 2021 perceptual image quality assessment challenge [23]. Our work will be an opportunity to further expand the approach for the perceptual IQA task.

关键词： Image quality Measurement Image resolution Head Feature extraction Quality assessment pattern recognition

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NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2022 Challenge on Stereo Image Super-Resolution: Metho...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Longguang Guo, Yulan Wang, Yingqian Li, Juncheng Gu, Shuhang Timofte, Radu Chen, Liangyu Chu, Xiaojie Yu, Wenqing Jin, Kai Wei, Zeqiang Guo, Sha Yang, Angulia Zhou, Xiuzhuang Guo, Guodong Dai, Bin Peng, Feiyue Xiao, Huaxin Yan, Shen Liu, Yuxiang Cai, Hanxiao Cao, Pu Nie, Yang Yang, Lu Song, Qing Hu, Xiaotao Xu, Jun Xu, Mai Jing, Junpeng Deng, Xin Xing, Qunliang Qiao, Minglang Guan, Zhenyu Guo, Wenlong Peng, Chenxu Chen, Zan Chen, Junyang Li, Hao Chen, Junbin Li, Weijie Yang, Zhijing Li, Gen Li, Aijin Sun, Lei Zhang, Dafeng Liu, Shizhuo Zhang, Jiangtao Qu, Yanyun Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Liang, Qiaohui Lin, Jianxin Wang, Yijun Yin, Lianying Zhang, Rongju Zhao, Wei Xiao, Peng Xu, Rongjian Zhang, Zhilu Zuo, Wangmeng Guo, Hansheng Gao, Guangwei Zeng, Tieyong Pi, Huicheng Zhang, Shunli Kim, Joohyeok Kim, HyeonA Park, Eunpil Sim, Jae-Young Zhai, Jucai Zeng, Pengcheng Liu, Yang Ma, Chihao Huang, Yulin Chen, Junying Natl Univ Defense Technol Changsha Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Univ Sydney Sydney NSW 2006 Australia Univ Wurzburg Wurzburg Germany Swiss Fed Inst Technol Zurich Switzerland MEGVII Technol Beijing Peoples R China Peking Univ Beijing Peoples R China Bigo Technol Pte Ltd Singapore Singapore Beijing Univ Posts & Telecommun Smart Healthcare Innovat Lab Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Baidu Res Inst Deep Learning Beijing Peoples R China Natl Univ Defense Technol Coll Syst Engn Changsha Peoples R China Natl Univ Defense Technol Coll Liberal Arts & Sci Changsha Peoples R China Beijing Univ Posts & Telecommun Pattern Recognit & Intelligent Vis Lab Beijing Peoples R China Nankai Univ Coll Comp Sci Tianjin Peoples R China Nankai Univ Sch Stat & Data Sci Tianjin Peoples R China Beihang Univ Beijing Peoples R China Zhejiang Univ Technol Hangzhou Zhejiang Peoples R China Guangdong Univ Technol Guangzhou Guangdong Peoples R China Tencent OVBU Wuhu Peoples R China SRC B Beijing Peoples R China Xiamen Univ Xiamen Peoples R China Natl Taiwan Univ Dept Elect Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China Harbin Inst Technol Harbin Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China Ulsan Natl Inst Sci & Technol Dept Elect Engn Ulsan South Korea Ulsan Natl Inst Sci & Technol Grad Sch Artificial Intelligence Ulsan South Korea Beijing Jiaotong Univ Beijing Peoples R China City Univ Hong Kong Hong Kong Peoples R China South China Univ Technol Guangzhou Guangdong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results. This challenge has 1 track aiming at the stereo image super-resolution problem under a standard bicubic degradation. In total, 238 participants were successfully registered, and 21 teams competed in the final testing phase. Among those participants, 20 teams successfully submitted results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR.

关键词： Degradation computer vision conferences Superresolution Benchmark testing pattern recognition Image restoration

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APES: Audiovisual Person Search in Untrimmed Video

APES: Audiovisual Person Search in Untrimmed Video

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Alcazar, Juan Leon Heilbron, Fabian Caba Mai, Long Perazzi, Federico Lee, Joon-Young Arbelaez, Pablo Ghanem, Bernard Univ Los Andes Bogota Colombia Adobe Res San Jose CA USA King Abdullah Univ Sci & Technol Thuwal Saudi Arabia

ISBN: (纸本)9781665448994

Humans are arguably one of the most important subjects in video streams, many real-world applications such as video summarization or video editing workflows often require the automatic search and retrieval of a person of interest. Despite tremendous efforts in the person re-identification and retrieval domains, few works have developed audiovisual search strategies. In this paper, we present the Audiovisual Person Search dataset (APES), a new dataset composed of untrimmed videos whose audio (voices) and visual (faces) streams are densely annotated. APES contains over 1.9K identities labeled along 36 hours of video, making it the largest dataset available for untrimmed audiovisual person search. A key property of APES is that it includes dense temporal annotations that link faces to speech segments of the same identity. To showcase the potential of our new dataset, we propose an audiovisual baseline and benchmark for person retrieval. Our study shows that modeling audiovisual cues benefits the recognition of people's identities.

关键词： Visualization computer vision Annotations conferences Streaming media Benchmark testing Search problems

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Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution

Leveraging Multi scale Backbone with Multilevel supervision ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Nathan, Sabari Kansal, Priya Couger Inc Shibuya Ku Tokyo Japan

ISBN: (纸本)9781665448994

This paper proposes an attention-based multi-level model with a multi-scale backbone for thermal image super-resolution. The model leverages the multi-scale backbone as well. The thermal image dataset is provided by PBVS 2020 in their thermal image super-resolution challenge. This dataset contains the images with three different resolution scales(low, medium, high) [1]. However, only the medium and high-resolution images are used to train the proposed architecture to generate the super-resolution images in x2, x4 scales. The proposed architecture is based on the Res2net blocks as the backbone of the network. Along with this, the coordinate convolution layer and dual attention are also used in the architecture. Further, multi-level supervision is implemented to supervise the output image resolution similarity with the real image at each block during training. To test the robustness of the proposed model, we evaluated our model on the Thermal-6 dataset [20]. The results show that our model is efficient to achieve state-of-the-art results on the PBVS dataset. Further the results on the Thermal-6 dataset show that the model has a decent generalization capacity.

关键词： Training Convolution conferences Superresolution computer architecture Robustness pattern recognition

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CoCon: Cooperative-Contrastive Learning

CoCon: Cooperative-Contrastive Learning

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Rai, Nishant Adeli, Ehsan Lee, Kuan-Hui Gaidon, Adrien Niebles, Juan Carlos Stanford Univ Stanford CA 94305 USA Toyota Res Inst Toyota Japan

ISBN: (纸本)9781665448994

Labeling videos at scale is impractical. Consequently, self-supervised visual representation learning is key for efficient video analysis. Recent success in learning image representations suggest contrastive learning is a promising framework to tackle this challenge. However, when applied to real-world videos, contrastive learning may unknowingly lead to separation of instances that contain semantically similar events. In our work, we introduce a cooperative variant of contrastive learning to utilize complementary information across views and address this issue. We use data-driven sampling to leverage implicit relationships between multiple input video views, whether observed (e.g. RGB) or inferred (e.g. flow, segmentation masks, poses). We are one of the firsts to explore exploiting inter-instance relationships to drive learning. We experimentally evaluate our representations on the downstream task of action recognition. Our method achieves competitive performance on standard benchmarks (UCF101, HMDB51, Kinetics400). Furthermore, qualitative experiments illustrate that our models can capture higher-order class relationships. The code is available at http://***/nishantrai18/CoCon.

关键词： computer vision Visualization Semantics Performance gain pattern recognition Noise measurement Labeling

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GOO: A Dataset for Gaze Object Prediction in Retail Environments

GOO: A Dataset for Gaze Object Prediction in Retail Environm...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tomas, Henri Reyes, Marcus Dionido, Raimarc Ty, Mark Mirando, Jonric Casimiro, Joel Atienza, Rowel Guinto, Richard Univ Philippines Quezon City Philippines Samsung R&D Inst Philippines Taguig Philippines

ISBN: (纸本)9781665448994

One of the most fundamental and information-laden actions humans do is to look at objects. However, a survey of current works reveals that existing gaze-related datasets annotate only the pixel being looked at, and not the boundaries of a specific object of interest. This lack of object annotation presents an opportunity for further advancing gaze estimation research. To this end, we present a challenging new task called gaze object prediction, where the goal is to predict a bounding box for a person's gazed-at object. To train and evaluate gaze networks on this task, we present the Gaze On Objects (GOO) dataset. GOO is composed of a large set of synthetic images (GOO-Synth) supplemented by a smaller subset of real images (GOO-Real) of people looking at objects in a retail environment. Our work establishes extensive baselines on GOO by re-implementing and evaluating selected state-of-the-art models on the task of gaze following and domain adaptation. Code is available(1) on github.

关键词： Training computer vision Adaptation models conferences Estimation computer architecture Benchmark testing

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Skeleton Aware Multi-modal Sign Language recognition

Skeleton Aware Multi-modal Sign Language Recognition

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Jiang, Songyao Sun, Bin Wang, Lichen Bai, Yue Li, Kunpeng Fu, Yun Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9781665448994

Sign language is commonly used by deaf or speech impaired people to communicate but requires significant effort to master. Sign Language recognition (SLR) aims to bridge the gap between sign language users and others by recognizing signs from given videos. It is an essential yet challenging task since sign language is performed with the fast and complex movement of hand gestures, body posture, and even facial expressions. Recently, skeleton-based action recognition attracts increasing attention due to the independence between the subject and background variation. However, skeleton-based SLR is still under exploration due to the lack of annotations on hand keypoints. Some efforts have been made to use hand detectors with pose estimators to extract hand key points and learn to recognize sign language via Neural Networks, but none of them outperforms RGB-based methods. To this end, we propose a novel Skeleton Aware Multi-modal SLR framework (SAM-SLR) to take advantage of multi-modal information towards a higher recognition rate. Specifically, we propose a Sign Language Graph Convolution Network (SL-GCN) to model the embedded dynamics and a novel Separable Spatial-Temporal Convolution Network (SSTCN) to exploit skeleton features. RGB and depth modalities are also incorporated and assembled into our framework to provide global information that is complementary to the skeleton-based methods SL-GCN and SSTCN. As a result, SAM-SLR achieves the highest performance in both RGB (98.42%) and RGB-D (98.53%) tracks in 2021 Looking at People Large Scale Signer Independent Isolated SLR Challenge. Our code is available at https://***/jackyjsy/cvpr21Chal-SLR

关键词： Convolution Annotations Assistive technology conferences Neural networks Gesture recognition Detectors

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SrvfNet: A Generative Network for Unsupervised Multiple Diffeomorphic Functional Alignment

SrvfNet: A Generative Network for Unsupervised Multiple Diff...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Nunez, Elvis Lizarraga, Andrew Joshi, Shantanu H. Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90024 USA Univ Calif Los Angeles Dept Bioengn Los Angeles CA 90024 USA Univ Calif Los Angeles Dept Neurol Ahmanson Lovelace Brain Mapping Ctr Los Angeles CA 90024 USA

ISBN: (纸本)9781665448994

We present SrvfNet, a generative deep learning framework for the joint multiple alignment of large collections of functional data comprising square-root velocity functions (SRVF) to their templates. Our proposed framework is fully unsupervised and is capable of aligning to a predefined template as well as jointly predicting an optimal template from data while simultaneously achieving alignment. Our network is constructed as a generative encoder-decoder architecture comprising fully-connected layers capable of producing a distribution space of the warping functions. We demonstrate the strength of our framework by validating it on synthetic data as well as diffusion profiles from magnetic resonance imaging (MRI) data.

关键词： Deep learning computer vision Magnetic resonance imaging conferences Computational modeling Training data computer architecture

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HINet: Half Instance Normalization Network for Image Restoration

HINet: Half Instance Normalization Network for Image Restora...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chen, Liangyu Lu, Xin Zhang, Jie Chu, Xiaojie Chen, Chengpeng MEGVII Technol Beijing Peoples R China Fudan Univ Shanghai Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9781665448994

In this paper, we explore the role of Instance Normalization in low-level vision tasks. Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks. Based on HIN Block, we design a simple and powerful multi-stage network named HINet, which consists of two subnetworks. With the help of HIN Block, HINet surpasses the state-of-the-art (SOTA) on various image restoration tasks. For image denoising, we exceed it 0.11dB and 0.28 dB in PSNR on SIDD dataset, with only 7.5% and 30% of its multiplier-accumulator operations (MACs), 6.8 x and 2.9x speedup respectively. For image deblurring, we get comparable performance with 22.5% of its MACs and 3.3 x speedup on REDS and GoPro datasets. For image deraining, we exceed it by 0.3 dB in PSNR on the average result of multiple datasets with 1.4x speedup. With HINet, we won the 1st place on the NTIRE 2021 Image Deblurring Challenge - Track2. JPEG Artifacts, with a PSNR of 29.70.

关键词： Visualization computer vision conferences Transform coding Image restoration pattern recognition Task analysis

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Multi-Modal Temporal Convolutional Network for Anticipating Actions in Egocentric Videos

Multi-Modal Temporal Convolutional Network for Anticipating ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zatsarynna, Olga Abu Farha, Yazan Gall, Juergen Univ Bonn Bonn Germany

ISBN: (纸本)9781665448994

Anticipating human actions is an important task that needs to be addressed for the development of reliable intelligent agents, such as self-driving cars or robot assistants. While the ability to make future predictions with high accuracy is crucial for designing the anticipation approaches, the speed at which the inference is performed is not less important. Methods that are accurate but not sufficiently fast would introduce a high latency into the decision process. Thus, this will increase the reaction time of the underlying system. This poses a problem for domains such as autonomous driving, where the reaction time is crucial. In this work, we propose a simple and effective multi-modal architecture based on temporal convolutions. Our approach stacks a hierarchy of temporal convolutional layers and does not rely on recurrent layers to ensure a fast prediction. We further introduce a multi-modal fusion mechanism that captures the pairwise interactions between RGB, flow, and object modalities. Results on two large-scale datasets of egocentric videos, EPIC-Kitchens-55 and EPIC-Kitchens-100, show that our approach achieves comparable performance to the state-of-the-art approaches while being significantly faster.

关键词： computer vision conferences computer architecture pattern recognition Autonomous automobiles Reliability Intelligent agents

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